scholarly journals Method of Wireless Sensor Network Data Fusion

2017 ◽  
Vol 13 (09) ◽  
pp. 114 ◽  
Author(s):  
Lili Ma ◽  
Jiangping Liu ◽  
Jidong Luo
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Information Fusion ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Large Data ◽  
Wireless Sensor ◽  
Fusion Algorithm

<p style="margin: 1em 0px;"><span lang="EN-US"><span style="font-family: 宋体; font-size: medium;">In order to better deal with large data information in computer networks, a large data fusion method based on wireless sensor networks is designed. Based on the analysis of the structure and learning algorithm of RBF neural networks, a heterogeneous RBF neural network information fusion algorithm in wireless sensor networks is presented. The effectiveness of information fusion processing methods is tested by RBF information fusion algorithm. The proposed algorithm is applied to heterogeneous information fusion of cluster heads or sink nodes in wireless sensor networks. The simulation results show the effectiveness of the proposed algorithm.  Based on the above finding, it is concluded that the RBF neural network has good real-time performance and small network delay. In addition, this method can reduce the amount of information transmission and the network conflicts and congestion.</span></span></p>

scholarly journals A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2555 ◽  
Author(s):  
Tengyue Zou ◽  
Yuanxia Wang ◽  
Mengyi Wang ◽  
Shouying Lin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Real Time ◽  
Wireless Sensor ◽  
Fusion Algorithm ◽  
Weighted Data

Research on Wireless Sensor Networks Data Fusion Algorithm Based on COA-BP

2014 ◽  
Vol 539 ◽  
pp. 247-250
Author(s):  
Xiao Xiao Liang ◽  
Li Cao ◽  
Chong Gang Wei ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Network Lifetime ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Bp Algorithm ◽  
Wireless Sensor

To improve the wireless sensor networks data fusion efficiency and reduce network traffic and the energy consumption of sensor networks, combined with chaos optimization algorithm and BP algorithm designed a chaotic BP hybrid algorithm (COA-BP), and establish a WSNs data fusion model. This model overcomes shortcomings of the traditional BP neural network model. Using the optimized BP neural network to efficiently extract WSN data and fusion the features among a small number of original date, then sends the extracted features date to aggregation nodes, thus enhance the efficiency of data fusion and prolong the network lifetime. Simulation results show that, compared with LEACH algorithm, BP neural network and PSO-BP algorithm, this algorithm can effectively reduce network traffic, reducing 19% of the total energy consumption of nodes and prolong the network lifetime.

scholarly journals A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 784 ◽  
Author(s):  
Jiayao Wang ◽  
Olamide Tawose ◽  
Linhua Jiang ◽  
Dongfang Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Base Station ◽  
Fuzzy Entropy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fusion Algorithm ◽  
Time Performance

The wireless sensor network (WSN) is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources. Therefore, energy consumption in wireless sensor networks is one of the most challenging problems in practice. On the other hand, data fusion can effectively decrease data redundancy, reduce the amount of data transmission and energy consumption in the network, extend the network life cycle, improve the utilization of bandwidth, and thus overcome the bottleneck on energy and bandwidth consumption. This paper proposes a new data fusion algorithm based on Hesitant Fuzzy Entropy (DFHFE). The new algorithm aims to reduce the collection of repeated data on sensor nodes from the source, and strives to utilize the information provided by redundant data to improve the data reliability. Hesitant fuzzy entropy is exploited to fuse the original data from sensor nodes in the cluster at the sink node to obtain higher quality data and make local decisions on the events of interest. The sink nodes periodically send local decisions to the base station that aggregates the local decisions and makes the final judgment, in which process the burden for the base station to process all the data is significantly released. According to our experiments, the proposed data fusion algorithm greatly improves the robustness, accuracy, and real-time performance of the entire network. The simulation results demonstrate that the new algorithm is more efficient than the state-of-the-art in terms of both energy consumption and real-time performance.

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